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Beacon-based sleep status and physical activity monitoring in humans
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Abstract
One out of every five people in Japan is dissatisfied with their sleep and various diseases caused by lack of exercise have been pointed out, but there are few effective remedies for these problems. In this study, we aimed to develop a simple method for measuring behavioral sleep patterns and physical activity using a beacon accelerometer wirelessly connected with a smartphone. A sleep prediction model was created comparing the data obtained from the accelerometer with the sleep status data obtained by a previously validated sleep monitoring system. The Random Forest model was able to classify sleep and wakefulness with a 97.4% and 85.4% precision, respectively, which were comparable to those of conventional acceleration-based sleep monitoring devices. Additionally, the same data acquisition method was used to classify exercise intensity into seven levels and a high correlation (r=0.813, p<0.0001) was found when comparing the classified exercise intensity to metabolic equivalent (MET) values. This suggests that the proposed method can be used for accurate measurement of both behavioral sleep and physical activity classifying over a long period of time.
... Devices and a data processing procedure followed our previous method used in human study [11]. In brief, a Bluetooth beacon accelerometer (MetamotinRL, Mbientlab Co. Ltd., CA, USA), an Android smartphone (Aquos Sense2, SHARP, Co, ltd, Tokyo, Japan), and Omron Sleep Meter HSL-102-M (Tokyo, Japan) were used. ...
... The three axes of the acceleration values were combined into a scalar value, which was then averaged over a one-sec interval. Following the previously reported procedure [11], standard deviation (SD) values of the scalar amount of acceleration per sec were calculated every 30 sec. The subsequent analysis involved utilizing five consecutive variables of SD data: two data points from one min before and two data points from one min after the target epoch. ...
... SAS Institute. Ltd, Cary, NC, USA), as reported previously [11], because it is based on probabilities of cluster membership rather than arbitrary cluster assignments based on boundaries. Regarding the number of clusters, we chose 7 clusters to facilitate comparison with metabolic equivalent of task (MET) scale in humans. ...
The sleep-wake cycle represents a crucial physiological process essential for maintaining homeostasis and promoting individual growth. In dogs, alterations in sleep patterns associated with age and dog’s correlation with temperament factors, such as nervousness, have been reported, and there is an increasing demand for precise monitoring of sleep and physical activity in dogs. The present study aims to develop an analysis method for measuring sleep-wake patterns and physical activity in dogs by utilizing an accelerometer and a smartphone. By analyzing time series data collected from the accelerometer attached to the dog's collar, a comprehensive sleep and activity analysis model was constructed. This model classified the activity level into seven classes and effectively highlighted the variations in sleep-activity patterns. Two classes with lower activity levels were considered as sleep, while other five levels were regarded as wake based on the rate of occurrence. This protocol of data acquisition and analysis provides a methodology that enables accurate and extended evaluation of both sleep and physical activity in dogs.
How people wake up and regain alertness in the hours after sleep is related to how they are sleeping, eating, and exercising. Here, in a prospective longitudinal study of 833 twins and genetically unrelated adults, we demonstrate that how effectively an individual awakens in the hours following sleep is not associated with their genetics, but instead, four independent factors: sleep quantity/quality the night before, physical activity the day prior, a breakfast rich in carbohydrate, and a lower blood glucose response following breakfast. Furthermore, an individual’s set-point of daily alertness is related to the quality of their sleep, their positive emotional state, and their age. Together, these findings reveal a set of non-genetic (i.e., not fixed) factors associated with daily alertness that are modifiable. In a prospective longitudinal study of 833 adults, we demonstrate that how you wake up and regain alertness in the hours after sleep is weakly associated with your genes. Instead, the modifiable factors of how you are sleeping, eating and exercising influence your return to full alertness, free of sleepiness.
Arm acceleration data have been used to measure sleep–wake rhythmicity. Although several methods have been developed for the accurate classification of sleep–wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named Acceleration-based Classification and Estimation of Long-term sleep–wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep–wake classification with high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.
Sleep disruption from causes, such as changes in lifestyle, stress from aging, family issues, or life pressures are a growing phenomenon that can lead to serious health problems. As such, sleep disorders need to be identified and addressed early on. In recent years, studies have investigated sleep patterns through body movement information collected by wristwatch-type devices or cameras. However, these methods capture only the individual’s awake and sleep states and lack sufficient information to identify specific sleep stages. The aim of this study was to use a 3-axis accelerometer attached to an individual’s head to capture information that can identify three specific sleep stages: rapid eye movement (REM) sleep, light sleep, and deep sleep. These stages are measured by heart rate features captured by a ballistocardiogram and body movement. The sleep experiment was conducted for two nights among eight healthy adult men. According to the leave-one-out cross-validation results, the F-scores were: awake 76.6%, REM sleep 52.7%, light sleep 78.2%, and deep sleep 67.8%. The accuracy was 74.6% for the four estimates. This proposed measurement system was able to estimate the sleep stages with high accuracy simply by using the acceleration in the individual’s head.
The commercial market for technologies to monitor and improve personal health and sports performance is ever expanding. A wide range of smart watches, bands, garments, and patches with embedded sensors, small portable devices and mobile applications now exist to record and provide users with feedback on many different physical performance variables. These variables include cardiorespiratory function, movement patterns, sweat analysis, tissue oxygenation, sleep, emotional state, and changes in cognitive function following concussion. In this review, we have summarized the features and evaluated the characteristics of a cross-section of technologies for health and sports performance according to what the technology is claimed to do, whether it has been validated and is reliable, and if it is suitable for general consumer use. Consumers who are choosing new technology should consider whether it (1) produces desirable (or non-desirable) outcomes, (2) has been developed based on real-world need, and (3) has been tested and proven effective in applied studies in different settings. Among the technologies included in this review, more than half have not been validated through independent research. Only 5% of the technologies have been formally validated. Around 10% of technologies have been developed for and used in research. The value of such technologies for consumer use is debatable, however, because they may require extra time to set up and interpret the data they produce. Looking to the future, the rapidly expanding market of health and sports performance technology has much to offer consumers. To create a competitive advantage, companies producing health and performance technologies should consult with consumers to identify real-world need, and invest in research to prove the effectiveness of their products. To get the best value, consumers should carefully select such products, not only based on their personal needs, but also according to the strength of supporting evidence and effectiveness of the products.
The purpose of this study was to formulate an algorithm for assessing sleep/waking from activity intensities measured with a waist-worn actigraphy, the Lifecorder PLUS (LC; Suzuken Co. Ltd., Nagoya, Japan), and to test the validity of the algorithm. The study consisted of 31 healthy subjects (M/F = 20/11, mean age 31.7 years) who underwent one night of simultaneous measurement of activity intensity by LC and polysomnography (PSG). A sleep(S)/wake(W) scoring algorithm based on a linear model was determined through discriminant analysis of activity intensities measured by LC over a total of 235 h and 56 min and the corresponding PSG-based S/W data. The formulated S/W scoring algorithm was then used to score S/W during the monitoring epochs (2 min each, 7078 epochs in total) for each subject. The mean agreement rate with the corresponding PSG-based S/W data was 86.9%, with a mean sensitivity (sleep detection) of 89.4% and mean specificity (wakefulness detection) of 58.2%. The agreement rates for the individual stages of sleep were 60.6% for Stage 1, 89.3% for Stage 2, 99.2% for Stage 3 + 4, and 90.1% for Stage REM. These results demonstrate that sleep/wake activity in young to middle-aged healthy subjects can be assessed with a reliability comparable to that of conventional actigraphy through LC waist actigraphy and the optimal S/W scoring algorithm.
It has long been known that sleep supports memory consolidation. Two recent studies now shed light on how sleep spindles, characteristic 11-16 Hz activity bursts, contribute critically to memory processing during the night.
This article provides a detailed overview of both factors influencing habitual physical activity, and relationships between such activity and health in the elderly. Current cross-sectional data from the Nakanojo Study, which we have been carrying out since 2000, indicate substantial associations between the overall health of participants, and both the year-averaged daily step count and the year-averaged daily duration of effort undertaken at an intensity >3 metabolic equivalents (MET). In men, the extent of health is associated more closely with the daily duration of activity >3 MET than with the daily step count, whereas in women the association is closer for the step count than for the duration of activity >3 MET. In both sexes, the threshold amount of physical activity associated with better health is greater for physical than for mental benefits: >8000 vs >4000 steps/day and/or >20 vs >5 min/day at >3 MET, respectively. In other words, physical health is better in those spending at least 20 min/day in moderate walking (at a pace of around 1.4 m/s [5 km/h]) and a further >60 min of light activity per day. In contrast, better mental health is associated with much smaller amounts of deliberate physical activity. Both the intensity and the total volume of physical activity are influenced by meteorological factors, particularly precipitation and mean ambient temperature. Activity decreases exponentially to about 4000 steps/day as precipitation increases. Excluding the influence of rainfall, the daily step count peaks at a mean outdoor temperature of around 17 degrees C; above and especially below such readings, physical activity decreases as a quadratic function of temperature. Seasonal changes in the microclimate should thus be considered when designing interventions intended to increase the habitual physical activity of older adults. Based on these findings, we are now developing preventive tactics that should contribute to health promotion, disease prevention and thus a reduction in medical expenses for elderly people.
The purpose of this study was to develop and validate automatic scoring methods to distinguish sleep from wakefulness based on wrist activity. Forty-one subjects (18 normals and 23 with sleep or psychiatric disorders) wore a wrist actigraph during overnight polysomnography. In a randomly selected subsample of 20 subjects, candidate sleep/wake prediction algorithms were iteratively optimized against standard sleep/wake scores. The optimal algorithms obtained for various data collection epoch lengths were then prospectively tested on the remaining 21 subjects. The final algorithms correctly distinguished sleep from wakefulness approximately 88% of the time. Actigraphic sleep percentage and sleep latency estimates correlated 0.82 and 0.90, respectively, with corresponding parameters scored from the polysomnogram (p < 0.0001). Automatic scoring of wrist activity provides valuable information about sleep and wakefulness that could be useful in both clinical and research applications.
The Ministry of Health, Labor and Welfare in Japan proposed a plan called “Health Japan 21,” which adopted sleep as one of the specific living habits needing improvement. This has led to increased interest in mental health needs at community public health sites. In addition, it was reported from a recent 2000 survey that one in five Japanese, and one in three elderly Japanese, suffer from insomnia. Insomnia is becoming a serious social problem; so much so that alarm bells are ringing with insomnia listed as one of the refractory diseases of the 21st century. Against this background, in January 2001, Japan began a national project called “Establishing a Science of Sleep.”
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